November 2017 Blog Posts (84)

While the skills required or expected from data scientists can vary based on the organization or domain they work in, being a data scientist can be viewed not merely as owning a set of skills, but also as having a certain mindset. In that sense I can differentiate between passive data scientists and active data scientists.

The passive data scientists will use the data they receive, or perform basic tasks to collect data stored in one or several central sources. Once they have the…

Data analysis will be invaluable to any student that is considering studying Statistics at University, College or High School. The book contains both Descriptive and 1-sample Inferential Statistics and is crammed with easy to understand examples, solved problems and supplementary questions.

The book also includes 5 complete trial exams with very detailed solutions. The trial exams contain a grading system that…

What is R Shiny App?

R shiny app is an interactive web interface. R shiny app has two components user interface object (UI.R) and server function (Server .R). The two components are passed as arguments to the shiny app function that creates a shiny app object. For more info on how to build Shiny…

The concepts of p-value and level of significance are vital components of hypothesis testing and advanced methods like regression. However, they can be a little tricky to understand, especially for beginners and good understanding of these concepts can go a long way in understanding advanced concepts in statistics and econometrics. Here, we try to simplify the concept in an easy, logical manner. Hope this helps.

Summary: Which is more important, the data or the algorithms? This chicken and egg question led me to realize that it’s the data, and specifically the way we store and process the data that has dominated data science over the last 10 years. And it all leads back to Hadoop.

Interest in the concept of measuring customer satisfaction is growing. More and more companies want to know how satisfied their customers are. At the same time an increasing number of decision-makers are realizing that measuring overall…

This is part of a new series of articles: once or twice a month, we post previous articles that were very popular when first published. These articles are at least 6 month old but no more than 12 month old. The previous digest in this series was posted here a while back.

This demo presents the RNNoise project, showing how deep learning can be applied to noise suppression. The main idea is to combine classic signal processing with deep learning to create a real-time noise suppression algorithm that's small and fast. No expensive GPUs required — it runs easily on a Raspberry Pi. The result is much simpler (easier to tune) and sounds…

The “three wise men” in the story of the nativity are believed to be Magi or Zoroastrians. As the story goes, these three wise men followed the Star of Bethlehem in search for the messiah. For the most part, the Zoroastrians supported the one-god concept - and they also believed in a messiah. It made sense for them - sometimes regarded as the original astrologers - to use their special talents to seek out the baby Jesus. It makes sense purely from a narrative standpoint; although to me…

Mobile usage has surpassed the desktop usage a few years back and is fast becoming consumer’s preferred portal to the internet. Always-on consumers spend more than 70% of their media consumption and screen time on mobile devices, and…

he following problems are taken from a few assignments from the coursera courses Introduction to Deep Learning (by Higher School of Economics) and Neural Networks and Deep Learning (by Prof Andrew Ng, deeplearning.ai). The problem descriptions are taken straightaway from the assignments.

In this post we will implement a simple 3-layer neural network from scratch. We won’t derive all the math that’s required, but I will try to give an intuitive explanation of what we are doing. I will also point to resources for you read up on the details.

Many of us are bombarded with various recommendations in our day to day life, be it on e-commerce sites or social media sites. Some of the recommendations look relevant but some create range of emotions in people, varying from confusion to anger.

There are basically two types of recommender systems, Content based and Collaborative filtering. Both have their pros and cons depending upon the…

One of the biggest problems in data management and data science is being able to obtain “good” data. You need to gather sufficient data from a substantial array of subjects who fit your study’s requirements, and ensure the accuracy of the data... otherwise, any conclusions you draw could be biased or skewed.

But assume for a moment that your data is already solid. That’s no guarantee of success, unfortunately: It’s like having all the ingredients of a pizza in one place but lacking…